That might work. Maybe have the adversarial network try to distinguish GPT-3 text from human text? That said, GPT-3 is already trying to predict humanlike text continuations, so there's a decent chance that having a separate GAN layer wouldn't help. It's probably worth doing the experiment though; traditional GANs work by improving the discriminator as well as the desired categorizer, so there's a chance it could work here too.
You might find this interesting:
https://www.gwern.net/GPT-2-preference-learning#bradley-terry-preference-learning
I am wondering if anyone tried to combine GPT-3 with GAN, basically trying to train network that would feed GPT-3 questions and then judge responses as correct/incorrect, thus providing GPT-3 opportunity to improve.
Does my question even make sense or I am far off base?